Al Nejmeh Football Team: An In-depth Analysis for Sports Bettors

Overview / Introduction about the Team

Al Nejmeh is a prominent football team based in Lebanon, competing in the Lebanese Premier League. Known for their strategic play and passionate fanbase, Al Nejmeh has established itself as a formidable force in Lebanese football. The team currently plays under the guidance of their experienced coach and is known for its dynamic 4-3-3 formation.

Team History and Achievements

Founded in 1968, Al Nejmeh boasts a rich history filled with numerous titles and accolades. They have won multiple Lebanese Premier League titles and have consistently been at the forefront of Lebanese football. Notable seasons include their championship-winning campaigns, where they demonstrated exceptional skill and teamwork.

Current Squad and Key Players

The current squad features several standout players who contribute significantly to the team’s success. Key players include:

• John Doe – Striker, known for his goal-scoring prowess.
• Jane Smith – Midfielder, renowned for her playmaking abilities.
• Mike Brown – Defender, crucial for maintaining a solid defense.

Team Playing Style and Tactics

Al Nejmeh employs a 4-3-3 formation, focusing on a balanced approach between attack and defense. Their strategies emphasize quick transitions and strong midfield control. Strengths include their tactical flexibility and resilience under pressure, while weaknesses may involve occasional lapses in defensive organization.

Interesting Facts and Unique Traits

The team is affectionately nicknamed “The Eagles” by their fans. They have a passionate fanbase known as “The Nejmiyin,” who are renowned for their unwavering support. Rivalries with teams like Sagesse FC add an extra layer of excitement to their matches.

List & Rankings of Players, Stats, or Performance Metrics

• ✅ John Doe – Top scorer with 15 goals this season.
• ❌ Mike Brown – Recent injury affecting performance.
• 🎰 Jane Smith – Consistent playmaker with 10 assists.
• 💡 Overall team ranking – 3rd in league standings.

Comparisons with Other Teams in the League or Division

In comparison to other top teams in the league, Al Nejmeh stands out due to their consistent performance and strategic depth. While teams like Sagesse FC may have stronger individual players, Al Nejmeh’s cohesive unit often gives them an edge in crucial matches.

Case Studies or Notable Matches

A breakthrough game that highlights Al Nejmeh’s capabilities was their stunning victory against Tripoli SC last season, where they secured a 3-0 win through strategic playmaking and defensive solidity.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

</tr <>: Hi there! I’m working on implementing an attention mechanism within my model’s forward pass method. Here’s the snippet I’m dealing with: python if self.use_attention: sorted_scores, sorted_indices = torch.sort( scores.squeeze(0), descending=True ) topk_scores = sorted_scores[: self.top_k] topk_indices = sorted_indices[: self.top_k] # Number of non-zero elements in batch (number of members) # Warning: Member IDs are starting from 1 nb_nonzero = torch.count_nonzero(member_ids_batch) # TODO: replace magic number with len(self.member_ids_entity) topk_member_ids = ( torch.index_select(member_ids_batch, 0, topk_indices).tolist()[: nb_nonzero] ) I understand that it sorts scores to find top-k indices and then selects corresponding member IDs based on those indices. However, I’m puzzled by the comment regarding replacing the magic number `len(self.member_ids_entity)`. Could you help me understand what this means? Should I be doing something specific here? Thanks! <>: python The comment about replacing the magic number suggests that `len(self.member_ids_entity)` should be used instead of an arbitrary number to determine how many member IDs you want to consider. This makes your code more flexible and maintainable because it ties the logic directly to your model’s configuration rather than relying on hardcoded values. In your snippet, you’re already using `self.top_k` to select the top scores and indices. If `self.top_k` is intended to be limited by `len(self.member_ids_entity)`, you might want to ensure that `self.top_k` does not exceed this length when initializing or setting it elsewhere in your code. Here’s a possible adjustment: python self.top_k = min(self.top_k, len(self.member_ids_entity)) This ensures that `self.top_k` is always within bounds relative to `self.member_ids_entity`. Adjusting this way helps prevent index errors and makes your code more robust. 😊 <>: Got it! So if I understand correctly, I should ensure that ‘self.top_k’ doesn’t exceed ‘len(self.member_ids_entity)’ by setting it like this: python self.top_k = min(self.top_k, len(self.member_ids_entity)) This way, I can avoid any potential index errors when selecting member IDs. Does this mean I should apply this logic right after initializing ‘self.top_k’, or is there a better place to handle this? <>: Sure! You should apply this logic right after initializing or setting `self.top_k`. This ensures that any subsequent operations using `self.top_k` are safe from index errors related to exceeding available member IDs. It’s best placed wherever you first define or update `self.top_k`, such as during initialization or configuration setup of your model class. <>: Alright! Now I’m thinking about making ‘top-k’ dynamic based on certain conditions during runtime instead of just setting it once during initialization. How could I implement logic that adjusts ‘top-k’ dynamically based on input characteristics or other criteria? Any suggestions on how to approach this? <>: To make ‘top-k’ dynamic during runtime, you can adjust it based on specific conditions or input characteristics within your forward pass method or wherever relevant logic resides. Here’s a basic approach: 1. **Define Criteria**: Determine what factors will influence ‘top-k’. This could be based on input size, specific thresholds in data characteristics (e.g., variance), or other domain-specific metrics. 2. **Implement Logic**: Within your forward pass or relevant method, calculate ‘top-k’ dynamically before using it: python def compute_dynamic_topk(input_data): # Example condition: adjust based on input size if input_data.size(0) > threshold: return min(max_topk_value, some_function_of_input(input_data)) else: return default_topk_value def forward_pass(…): … self.top_k = compute_dynamic_topk(input_data) … 3. **Ensure Bounds**: Always ensure ‘top-k’ does not exceed `len(self.member_ids_entity)`: python self.top_k = min(self.compute_dynamic_topk(input_data), len(self.member_ids_entity)) This approach allows flexibility while maintaining safety checks against index errors. <>: Great! So if I want to adjust ‘top-k’ dynamically based on input characteristics like variance or size during runtime, should I implement a function like `compute_dynamic_topk` within my forward pass? Here’s what I’m thinking: python class MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() # Initialize other parameters def compute_dynamic_topk(self, input_data): # Example condition: adjust based on input size if input_data.size(0) > threshold: return min(max_topk_value, some_function_of_input(input_data)) else: return default_topk_value def forward_pass(self, input_data): # Compute dynamic top-k self.top_k = min(self.compute_dynamic_topk(input_data), len(self.member_ids_entity)) # Continue with processing using dynamic top-k Does this look like a good approach? Any tips on refining this logic further? <>: Your approach looks solid! Implementing `compute_dynamic_topk` within your class allows for flexibility while keeping everything organized. Here are a few tips to refine it further: 1. **Parameterize Thresholds**: Consider passing thresholds (`threshold`, `max_topk_value`, etc.) as parameters during initialization so they can be easily adjusted without modifying the function directly. python def __init__(self, threshold=1000, max_topk_value=50): super(MyModel, self).__init__() self.threshold = threshold self.max_topk_value = max_topk_value 2. **Use Descriptive Functions**: For clarity and reusability (`some_function_of_input`), define helper functions outside if they involve complex calculations. 3. **Logging/Debugging**: Add logging statements inside `compute_dynamic_topk` to track how ‘top-k’ changes with different inputs during development. 4. **Testing**: Ensure thorough testing with various input scenarios to validate that dynamic adjustments behave as expected. By incorporating these refinements, your model will be more robust and adaptable! 🚀<>: Hi there, I’ve been trying to implement an image preprocessing pipeline using PyTorch transforms for my project involving both training data augmentation and validation preprocessing steps for different datasets like CIFAR10/CIFAR100/SVHN/ImageNet16_120/STL10/WikiArt/Cars196/CelebA/LSUN-Church/CUB200/COCO2014/MNIST/FMNIST/KMNIST/QMNIST/EMNIST/BreastHistology/HAM10000/ISIC2018/Skin Lesion Challenge ISIC Archive/Caltech101/Caltech256/DogFaceDataset/DTD/DogDataset/Florence/PlantSeedlings/VOC2007/VOC2012/Sun397/Food101/YFCC100M/FiveKWikiText103/PennTreebank/WikiText103/OpenImages/PascalContext/PascalContextExtended/COCOStuff10K/SBU/CLEVR/MPI3D/Landmarks/WiderFace/WiderFace_Extra/HMDB51/Kinetics400/UCF101/JHMDB21/IkeaIKEA046v5/MPII/VIVA/NUSWide/NYT24K/SemGeos/SemWeb2016/TACoS/UT-Zap50K/UrbanSound8K/MapillaryVistas). However I keep running into an issue when trying to run my code which involves applying these transforms correctly depending on whether we’re dealing with training data augmentation or validation preprocessing steps. Here’s the buggy version of my code snippet: python import random def get_transforms(dataset_name=None, data_root=None, image_size=None, train_transform_types=None, train_transform_args=None, val_transform_types=None, val_transform_args=None, test_transform_types=None, test_transform_args=None, download_datasets=False): if dataset_name == None: raise Exception(“dataset_name must be specified.”) import torchvision.transforms as transforms train_dataset_has_gt_bboxes = False train_dataset_has_gt_masks = False train_dataset_has_gt_keypoints = False val_dataset_has_gt_bboxes = False val_dataset_has_gt_masks = False val_dataset_has_gt_keypoints = False def get_train_val_test_transforms(dataset_name=’cifar10′, image_size=32): import torchvision.transforms as transforms normalize_cifar10_train_stats={‘mean’: [x / 255 for x in [125.31 ,124.96 ,123.38]], ‘std’: [x /255 for x in [63.0 ,62.69 ,63 .27]]} normalize_imagenet_train_stats={‘mean’:[0.,0.,0.] , ‘std’:[1.,1.,1.]} normalize_cifar100_train_stats={‘mean’: [x /255 for x in [129.31 ,124.78 ,112.95]], ‘std’: [x /255 for x in [68.09 ,65 .03 ,70 .67]]} normalize_svhn_train_stats={‘mean’: [x /255 for x in [109.,109.,109.] ], ‘std’: [x /255 for x in [50.,50.,50.]]} import numpy as np normalize_cifar10_val_stats=np.load(‘./datasets/cifar10_stats.npy’,allow_pickle=True).item() normalize_imagenet_val_stats=np.load(‘./datasets/imagenet_stats.npy’,allow_pickle=True).item() normalize_cifar100_val_stats=np.load(‘./datasets/cifar100_stats.npy’,allow_pickle=True).item() normalize_svhn_val_stats=np.load(‘./datasets/svhn_stats.npy’,allow_pickle=True).item() train_transform_types=[‘RandomResizedCropOrRandomCrop’,’ColorJitter’,’RandomHorizontalFlip’,’ConvertToTensor’,’Normalize’] train_transform_args=[{‘size’:image_size,’scale’:[0.08,.99],’ratio’:[0,.9999]} , {‘brightness’:0,’contrast’:8,’saturation’:8,’hue’:(.8*8)/360}, {}, {}, {‘mean’:[x /255 for x in [125.,124.,123]],’std’:[x /255 for x in [63.,62.,63]]}] val_transform_types=[‘ResizeOrPadAndRandomCropOrCenterCrop’,’ConvertToTensor’,’Normalize’] val_transform_args=[{‘size’:image_size,’scale’:[0,.9999],’ratio’:[0,.9999]}, {}, {‘mean’:[x /255 for x in [125.,124.,123]],’std’:[x /255 for x in [63.,62.,63]]}] test_transform_types=[‘ResizeOrPadAndCenterCrop’,’ConvertToTensor’,’Normalize’] test_transform_args=[{‘size’:image_size}, {}, {‘mean’:[x /255 for x in [125.,124.,123]],’std’:[x /255 for x in [63.,62.,63]]}] import numpy as np normalize_cifar10_test_stats=np.load(‘./datasets/cifar10_stats.npy’,allow_pickle=True).item() normalize_imagenet_test_stats=np.load(‘./datasets/imagenet_stats.npy’,allow_pickle=True).item() normalize_cifar100_test_stats=np.load(‘./datasets/cifar100_stats.npy’,allow_pickle=True).item() normalize_svhn_test_stats=np.load(‘./datasets/svhn_stats.npy’,allow_pickle=True).item() train_augmentations=[] val_augmentations=[] test_augmentations=[] augmentations={} for i,tr_type_str,tr_arg_dict_list_or_tuple_or_set_or_other_seq_type in zip(train_transform_types, train_transform_args): tr_arg_dict_list_or_tuple_or_set_or_other_seq_type= tr_arg_dict_list_or_tuple_or_set_or_other_seq_type[()] assert isinstance(tr_arg_dict_list_or_tuple_or_set_or_other_seq_type,(list,tuple,set)) tr_arg_dicts=[dict(t) if not isinstance(t,type(None)) else {} if isinstance(t,(str,int,float)) else t if isinstance(t,(dict,list,tuple,set)) else dict() for t in tr_arg_dict_list_or_tuple_or_set_or_other_seq_type] assert all([isinstance(t,dtype=dict) for t in tr_arg_dicts]) for i,tr_type_str,tr_arg_dict in zip(train_transform_types, train_transform_args): if dataset_name==’cifar10′: else: raise Exception(“dataset_name “+dataset_name+” unknown.”) for i,tr_type_str,tr_arg_dict in zip(val_transform_types, val_transform_args): if dataset_name==’cifar10′: else: raise Exception(“dataset_name “+dataset_name+” unknown.”) for i,tr_type_str,tr_arg_dict in zip(test_transform_types, test_transform_args): if dataset_name==’cifar10′: else: raise Exception(“dataset_name “+dataset_name+” unknown.”) return train_augmentations,val_augmentations,test_augmentations,augmentations if __name__ == ‘__main__’: When running this code snippet above,I get stuck at some point which results into following error traceback : Traceback (most recent call last): File “transform_pipeline.py”, line XX,in get_train_val_test_transfroms(): train_augmentations,val_augmentations,test_augmentations,augmentation=get_transfroms(dataset_nam=’cifar_102′, imge_size=32) File “transform_pipeline.py”, line YY,in get_transfroms(): assert all([isinstance(t,dtype=dict) for t in tr_arg_dicts]) AssertionError During handling ofthe above exception,the following exception was thrown: File “transform_pipeline.py”, line ZZ,in main(): train_augmenation,val_augmentation,test_aumgntation,augmentation=get_train_val_test_transfroms() Exception:”Traceback(most recent call last): n File “transform_pipeline.py”,line XX,in get_transfroms():n AssertionErrorn During handlingofthe above exception,the following exception was thrown:n File “transform_pipeline.py”,line ZZ,in main():n Exception:n Traceback(most recent call last):n File “transform_pipeline.py”,line XX,in get_transfroms():n AssertionError” Can anyone help me figure out what went wrong here? Why am I getting an AssertionError relatedto type checking? Thanks! <>: It looks like you’re encountering an assertion error because one of the elements in `tr_arg_dicts` is not being recognized as a dictionary (`dict`). This happens at the line where you check: python assert all([isinstance(t,dtype=dict) for t in tr_arg_dicts]) Here are some steps you can take to debug this issue: 1. **Check Input Types:** Ensure that each element passed into `train_transform_args`, `val_transform_args`, and `test_transform_args` is indeed convertible into a dictionary format. 2. **Inspect Data Structure:** Print out each element before converting them into dictionaries to see what types they actually are: python print(tr_arg_dict_list_or_tuple_or_set_or_other_seq_type) 3. **Modify Conversion Logic:** If some elements aren’t dictionaries but need special handling (like lists), ensure they are converted appropriately before asserting. 4. **Add Debugging Prints:** Add print statements before assertions to see which element fails: python print([type(t) for t in tr_arg_dicts]) 5. **Review Function Calls:** Ensure that when calling functions like `get_train_val_test_transfroms()`, all arguments are correctly structured according to expectations. By following these steps, you should be able to identify why one of your elements isn’t being recognized as a dictionary and fix it accordingly. <>: Alright ,I think I need to focus on ensuring all elements passed into transform arguments are properly formatted as dictionaries before asserting them as such.I’ll add some debugging prints to inspect the types of elements being processed.Here’s what I’m thinking: python for i,tr_type_str,tr_arg_dict_list_or_tuple_or_set_or_other_seq_type in zip(train_transform_types, train_transform_args): print(tr_arg_dict_list_or_tuple_or_set_or_other_seq_type) tr_arg_dict_list_or_tuple_or_set_or_other_seq_type= tr_arg_dict_list_or_tuple_or_set_or_other_seq_type[()] assert isinstance(tr_arg_dict_list_or_tuple_or_set_or_other_seq_type,(list,tuple,set)) tr_arg_dicts=[dict(t) if not isinstance(t,type(None)) else {} if isinstance(t,(str,int,float)) else t if isinstance(t,(dict,list,tuple,set)) else dict() for t in tr_arg_dict_list_or_tuple_or_set_or_other_seq_type] print([type(t) for t in tr_arg_dicts]) # Debugging print assert all([isinstance(t,dtype=dict) for t in tr_arg_dicts]) Does this approach make sense? Any other suggestions? <>: Your approach makes sense! Adding debugging prints will help identify any unexpected types before conversion attempts fail at assertion time. Here are a few additional suggestions: 1. **Detailed Logging:** Consider adding more detailed logging messages indicating which transformation type is currently being processed along with its index position. python print(f”Processing {i}: {tr_type_str}”) 2. **Handle Edge Cases:** Make sure any special cases (like empty lists/tuples) are handled gracefully before conversion attempts. 3. **Validate Inputs Early:** Before entering loops or complex transformations, validate inputs upfront if possible. 4. **Use Try-Except Blocks:** Wrap potentially problematic sections with try-except blocks temporarily during debugging to catch exceptions without stopping execution immediately. Implementing these strategies should help pinpoint where things go awry more effectively! 😊 <>: Great! I’ll add those debugging prints along with detailed logging messages indicating which transformation type is being processed along with its index position.Here’s how it looks now: python for i,tr_type_str,tr_arg_dict_list_or_tuple_in_zip(train_train_tranform_types, train_tranform_args): print(f”Processing {i}: {tr_typ_str}”) print(tr_ar_gdct_lis_tupl_eor_seta_r_ty_d) tr_ar_gdct_lis_tupl_eor_seta_r_ty_d= tr_ar_gdct_lis_tupl_eor_seta_r_ty_d[()] assert isinstance(tr_ar_gdct_lis_tupl_eor_seta_r_ty_d,(list,tuple,set)) tra_rg_dcts=[dict(t) if not isinstance(t,type(None)) else {} if isinstance(t,(str,int,float)) else t if isinstance(t,(dict,list,tuple,set)) else dict() for t in tra_rg_dcts] print([type(t) fr_in_t tra_rg_dcts]) # Debugging print assert all([isinstance(tra_rg_dcts,dtype=dict) for tra_rg_dcts in tra_rg_dcts]) Does this look good? Should I also wrap sections with try-except blocks temporarily? <>: Your updated code looks good! Adding detailed logging will definitely help trace issues more effectively. Wrapping sections with try-except blocks temporarily can be beneficial during debugging because it allows you to catch exceptions without halting execution immediately; however, make sure not overusing them once you’ve identified issues so they don’t mask other potential problems later on. Here’s how you might incorporate try-except blocks: python try: print(f”Processing {i}: {tr_typ_str}”) print(tr_ar_gdct_lis_tupl_eor_seta_r_ty_d) tr_ar_gdct_lis_tupl_eor_seta_r_ty_d = tr_ar_gdct_lis_tupl_eor_seta_r_ty_d[()] assert isinstance(tr_ar_gdct_lis_tupl_eor_seta_r_ty_d,(list,tuple,set)) except Exception as e: print(f”Error processing transformation {i}: {e}”) try: tra_rg_dcts=[dict(t) if not isinstance(t,type(None)) else {} if isinstance(t,(str,int,float)) else t if isinstance(t,(dict,list,tuple,set)) else dict() for t in tra_rg_dcts] except Exception as e: print(f”Error converting arguments at index {i}: {e}”) try: print([type(t) fr_in_t tra_rg_dcts]) # Debugging print assert all([isinstance(tra_rg_dcts,dtype=dict) for tra_rg_dcts in tra_rg_dcts]) except AssertionError: print(f”Assertion failed at index {i}. Check argument types.”) except Exception as e: print(f”Unexpected error at index {i}: {e}”) This setup will help catch specific issues without stopping execution abruptly. <>: Awesome! Now that I’ve added detailed logging messages indicating which transformation type is being processed along with its index position—and wrapped sections with try-except blocks—I’m curious about how these changes might affect performance when scaling up the number of transformations significantly larger datasets like ImageNet16_120 might require handling hundreds of transformations efficiently.Is there anything specific I should consider optimizing further? <>: When scaling up transformations for large datasets like ImageNet16_120, consider these optimizations: 1. **Batch Processing:** Process images/batches instead of individual transformations where possible to leverage parallel processing capabilities. 2. **Efficient Data Structures:** Use efficient data structures like NumPy arrays instead of lists when applicable; they offer faster operations due to optimized C implementations. 3. **Lazy Evaluation:** Use lazy evaluation techniques where transformations are applied only when needed rather than upfront processing. 4. **Caching Results:** Cache results of expensive computations if they’re reused across multiple images/batches. 5. **Profile Code:** Use profiling tools (e.g., cProfile) to identify bottlenecks specifically related to transformation application times. 6**Parallelism Libraries:** Utilize libraries like PyTorch’s DataLoader with multiple workers (`num_workers`) set appropriately based on CPU cores available; consider GPU acceleration if applicable. 7**Memory Management:** Monitor memory usage closely; use memory-efficient data formats (e.g., half precision floats). Implementing these strategies can significantly enhance performance when dealing with large-scale datasets. <>: Perfect tips! I’ll start by profiling my current setup using cProfile to pinpoint any bottlenecks first thing tomorrow morning.Then maybe explore parallel processing options since ImageNet16_120 could really benefit from those optimizations.Thanks again! <>: You’re welcome! Profiling first is a great step—it’ll give you clear insights into where optimizations will have the most impact 📈 . Exploring parallel processing next will likely yield significant improvements given ImageNet16_120’s scale 🚀 . Good luck with your optimizations—feel free reach out if you need more assistance along the way! 😊<>: Hey there, I’m having some trouble understanding why my implementation isn’t working correctly when trying out different optimization algorithms using TensorFlow Probability optimizers integrated into Keras models via custom training loops over multiple epochs across various devices (CPU/GPU). Here’s my current implementation attempt: python def _test_dense_layer_optimizers( lr, weight_decay, momentum, beta_1, beta_2, epsilon, centered_momentum, gradient_clipnorm, gradient_cliplayer_norm): “””Tests dense layer optimizers.””” mitigation_dim = max(len(weight_decay), len(momentum), len(beta_1), len(beta_2), len(epsilon), len(centered_momentum)) mitigation_range = list(range(mitigation_dim)) weight_decay_values = weight_decay + ([weight_decay[-1]] * (mitigation_dim – len(weight_decay))) momentum_values = momentum + ([momentum[-1]] * (mitigation_dim – len(momentum))) beta_1_values = beta_1 + ([beta_1[-1]] * (mitigation_dim – len(beta_1))) beta_2_values = beta_2 + ([beta_2[-1]] * (mitigation_dim – len(beta_2))) epsilon_values = epsilon + ([epsilon[-1]] * (mitigation_dim – len(epsilon))) centered_momentum_values = centered_momentum + ( [centered_momentum[-1]] * (mitigation_dim – len(centered_momentum))) hvd.init() num_replicas_hvd = hvd.size() optimizer_configs_to_run_per_device.clear() deviceType.set_visible_devices([], ”) deviceType.__setattr__(‘_visible_devices’, []) os.environ[‘CUDA_VISIBLE_DEVICES’] = ” config_index += 6 * mitigation_dim * num_layers * num_replicas_hvd**3 + ( num_layers * num_replicas_hvd**3) key_to_optimizer_result.clear() num_gpu_devices_to_use.clear() tf.keras.backend.clear_session() # # Run tests across multiple GPUs. # num_gpu_devices_to_use.append(0) deviceType.set_visible_devices([‘/gpu:’ + str(i)] for i in range(num_gpu_devices_to_use[0])) deviceType.__setattr__(‘_visible_devices’, [‘/gpu:’ + str(i) for i in range(num_gpu_devices_to_use[0])]) os.environ[‘CUDA_VISIBLE_DEVICES’] = str(list(range(num_gpu_devices_to_use[0]))) tf.keras.backend.clear_session() global_strategy_cpu_for_multi_gpu_tests.set_config( sess_config=tf.compat.v1.ConfigProto(device_count={‘GPU’: int(os.environ[‘CUDA_VISIBLE_DEVICES’].split(‘,’)[num_replica_index_per_iteration])})) cfgDict[‘strategy’] = global_strategy_cpu_for_multi_gpu_tests._impl.get_config()[ ‘_serialize’] global_strategy_cpu_for_multi_gpu_tests._impl.set_config(cfgDict[‘strategy’]) counter += int((num_gpu_devices_to_use[0] == args.num_gpus_per_worker)**args.num_parallel_calls_per_worker) _config_proto_builder_counter += int((num_gpu_devices_to_use[0] == args.num_gpus_per_worker)**args.num_parallel_calls_per_worker) # # Test Keras Dense layer optimizers using TensorFlow Probability optimizers. # per_device_iterator.append(_iterative_process_runner()) per_device_iterator[counter].process(optimizer_configs_to_run_per_device[ config_index], config_index) key_to_optimizer_result.update(per_device_iterator[counter].get_results()) # # Run tests across CPUs. # config_index += mitigation_dim * num_layers * num_replicas_hvd**3 // args.chief_split_factor // args.checkpoint_factor // args.mtf_cross_replica_sum_factor // args.mtf_reduce_mean_factor // args.tensor_core_factor // args.bfloat16_factor // args.loss_scale_factor // args.virtual_batch_size_factor // args.pipeline_slice_factor // args.batch_group_assignment_factor**args.num_parallel_calls_per_worker//args.variable_update_frequency_factor//args.activation_checkpointing_frequency_factor//args.recompute_gradients_frequency_factor//args.lite_mode_inter_op_parallelism_threads//args.lite_mode_intra_op_parallelism_threads//args.lite_mode_num_threads//int(args.enable_xla)**args.xla_num_partitions//int(args.enable_xla_offload)**args.xla_num_partitions//int(args.enable_mlir_bridge)**int(args.enable_mlir_bridge)+6*mitigation_dim*num_layers*num_replicas_hvd**3*(not(args.VariableUpdateFrequency % args.CheckpointFactor == int(os.environ.get(‘CHECKPOINT_AND_VARIABLE_UPDATE_FREQ_EQUATED_FLAG’))))*(not(args.MixedPrecisionLossScaleManagerLossScaleScheduleFactor % args.CheckpointFactor == int(os.environ.get(‘CHECKPOINT_AND_VARIABLE_UPDATE_FREQ_EQUATED_FLAG’))))*(not(args.CrossReplicaSumFactor % args.CheckpointFactor == int(os.environ.get(‘CHECKPOINT_AND_VARIABLE_UPDATE_FREQ_EQUATED_FLAG’))))*(not(args.MixedPrecisionReduceMeanFactor % args.CheckpointFactor == int(os.environ.get(‘CHECKPOINT_AND_VARIABLE_UPDATE_FREQ_EQUATED_FLAG’))))*(not(args.TensorCoreFactor % args.CheckpointFactor == int(os.environ.get(‘CHECKPOINT_AND_VARIABLE_UPDATE_FREQ_EQUATED_FLAG’))))*(not(args.Bfloat16Factor % args.CheckpointFactor == int(os.environ.get(‘CHECKPOINT_AND_VARIABLE_UPDATE_FREQ_EQUATED_FLAG’))))*(not(args.VirtualBatchSizeFactor % args.CheckpointFactor == int(os.environ.get(‘CHECKPOINT_AND_VARIABLE_UPDATE_FREQ_EQUATED_FLAG’))))*(not(args.PipelineSliceFactor % args.CheckpointFactor == int(os.environ.get(‘CHECKPOINT_AND_VARIABLE_UPDATE_FREQ_EQUATED_FLAG’))))*int((num_gpu_devices_to_use[0] != args.num_gpus_per_worker)**args.num_parallel_calls_per_worker)*counter*(_config_proto_builder_counter+int((num_gpu_devices_to_use[0] != args.num_gpus_per_worker)**args.num_parallel_calls_per_worker))*int(int(not(args.VariableUpdateFrequency % args.MTFCrossReplicaSumFactor == int(os.environ.get(‘MTF_CROSS_REPLICA_SUM_AND_VAR_UPDATES_EQUATED_FACTOR_FLAG’))))*int(not(args.MixedPrecisionLossScaleManagerLossScaleScheduleFactor % args.MTFCrossReplicaSumFactor == int(os.environ.get(‘MTF_CROSS_REPLICA_SUM_AND_VAR_UPDATES_EQUATED_FACTOR_FLAG’))))*int(not(args.CrossReplicaSumFactor % args.MTFCrossReplicaSumFactor == int(os.environ.get(‘MTF_CROSS_REPLICA_SUM_AND_VAR_UPDATES_EQUATED_FACTOR_FLAG’))))*int(not(args.MixedPrecisionReduceMeanFactor % args.MTFCrossReplicaSumFactor == int(os.environ.get(‘MTF_CROSS_REPLICA_SUM_AND_VAR_UPDATES_EQUATED_FACTOR_FLAG’))))*int(not(args.TensorCoreFactor % args.MTFCrossReplicaSumFactor == int(os.environ.get(‘MTF_CROSS_REPLICA_SUM_AND_VAR_UPDATES_EQUATED_FACTOR_FLAG’))))*int(not(args.Bfloat16Factor % args.MTFCrossReplicaSumFactor == int(os.environ.get(‘MTF_CROSS_REPLICA_SUM_AND_VAR_UPDATES_EQUATED_FACTOR_FLAG’))))*int(not(args.VirtualBatchSizeFactor % args.MTFCrossReplicaSumFactor == int(os.environ.get(‘MTF_CROSS_REPLICA_SUM_AND_VAR_UPDATES_EQUATED_FACTOR_FLAG’))))*int(not(args.PipelineSliceFactor % args.MTFCrossReplicaSumFactor == int(os.environ.get(‘MTF_CROSS_REPLICA_SUM_AND_VAR_UPDATES_EQUATED_FACTOR_FLAG’)))))# noqa E128,E501 pylint:disable=line-too-long,E501 pylint:disable=line-too-long,E128,W504,W505,C0305,C0306,C0330,C0325,C0306,R0914,R0915,R0916,R1718,R1729,R0915,R0914,R0915,W503,W504,W505,E128,E501 pylint:disable=line-too-long,E128,W503,W504,W505,E501 pylint:disable=line-too-long,E128,W503,W504,W505,E501 pylint:disable=line

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